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Protein sequence alignment is essential for a variety of tasks such as homology modeling and active site prediction. Alignment errors remain the main cause of low-quality structure models. A bioinformatics tool to refine alignments is needed to make protein alignments more accurate.

Results

We developed the SFESA web server to refine pairwise protein sequence alignments. Compared to the previous version of SFESA, which required a set of 3D coordinates for a protein, the new server will search a sequence database for the closest homolog with an available 3D structure to be used as a template. For each alignment block defined by secondary structure elements in the template, SFESA evaluates alignment variants generated by local shifts and selects the best-scoring alignment variant. A scoring function that combines the sequence score of profile-profile comparison and the structure score of template-derived contact energy is used for evaluation of alignments. PROMALS pairwise alignments refined by SFESA are more accurate than those produced by current advanced alignment methods such as HHpred and CNFpred. In addition, SFESA also improves alignments generated by other software.

Conclusions

SFESA is a web-based tool for alignment refinement, designed for researchers to compute, refine, and evaluate pairwise alignments with a combined sequence and structure scoring of alignment blocks. To our knowledge, the SFESA web server is the only tool that refines alignments by evaluating local shifts of secondary structure elements. The SFESA web server is available at http://prodata.swmed.edu/sfesa.

Motivation: Template-based modeling, including homology modeling and protein threading, is the most reliable method for protein 3D structure prediction. However, alignment errors and template selection are still the main bottleneck for current template-base modeling methods, especially when proteins under consideration are distantly related.

Results: We present a novel context-specific alignment potential for protein threading, including alignment and template selection. Our alignment potential measures the log-odds ratio of one alignment being generated from two related proteins to being generated from two unrelated proteins, by integrating both local and global context-specific information. The local alignment potential quantifies how well one sequence residue can be aligned to one template residue based on context-specific information of the residues. The global alignment potential quantifies how well two sequence residues can be placed into two template positions at a given distance, again based on context-specific information. By accounting for correlation among a variety of protein features and making use of context-specific information, our alignment potential is much more sensitive than the widely used context-independent or profile-based scoring function. Experimental results confirm that our method generates significantly better alignments and threading results than the best profile-based methods on several large benchmarks. Our method works particularly well for distantly related proteins or proteins with sparse sequence profiles because of the effective integration of context-specific, structure and global information.

Constructing a model of a query protein based on its alignment to a homolog with experimentally determined spatial structure (the template) is still the most reliable approach to structure prediction. Alignment errors are the main bottleneck for homology modeling when the query is distantly related to the template. Alignment methods often misalign secondary structural elements by a few residues. Therefore, better alignment solutions can be found within a limited set of local shifts of secondary structures. We present a refinement method to improve pairwise sequence alignments by evaluating alignment variants generated by local shifts of template-defined secondary structures. Our method SFESA is based on a novel scoring function that combines the profile-based sequence score and the structure score derived from residue contacts in a template. Such a combined score frequently selects a better alignment variant among a set of candidate alignments generated by local shifts and leads to overall increase in alignment accuracy. Evaluation of several benchmarks shows that our refinement method significantly improves alignments made by automatic methods such as PROMALS, HHpred and CNFpred. The web server is available at http://prodata.swmed.edu/sfesa.

Protein threading is one of the most successful protein structure prediction methods. Most protein threading methods use a scoring function linearly combining sequence and structure features to measure the quality of a sequence-template alignment so that a dynamic programming algorithm can be used to optimize the scoring function. However, a linear scoring function cannot fully exploit interdependency among features and thus, limits alignment accuracy.

This paper presents a nonlinear scoring function for protein threading, which not only can model interactions among different protein features, but also can be efficiently optimized using a dynamic programming algorithm. We achieve this by modeling the threading problem using a probabilistic graphical model Conditional Random Fields (CRF) and training the model using the gradient tree boosting algorithm. The resultant model is a nonlinear scoring function consisting of a collection of regression trees. Each regression tree models a type of nonlinear relationship among sequence and structure features. Experimental results indicate that this new threading model can effectively leverage weak biological signals and improve both alignment accuracy and fold recognition rate greatly.

Native structures of proteins are formed essentially due to the combining effects of local and distant (in the sense of sequence) interactions among residues. These interaction information are, explicitly or implicitly, encoded into the scoring function in protein structure prediction approaches—threading approaches usually measure an alignment in the sense that how well a sequence adopts an existing structure; while the energy functions in Ab Initio methods are designed to measure how likely a conformation is near-native. Encouraging progress has been observed in structure refinement where knowledge-based or physics-based potentials are designed to capture distant interactions. Thus, it is interesting to investigate whether distant interaction information captured by the Ab Initio energy function can be used to improve threading, especially for the weakly/distant homologous templates.

Results

In this paper, we investigate the possibility to improve alignment-generating through incorporating distant interaction information into the alignment scoring function in a nontrivial approach. Specifically, the distant interaction information is introduced through employing an Ab Initio energy function to evaluate the “partial” decoy built from an alignment. Subsequently, a local search algorithm is utilized to optimize the scoring function.

Experimental results demonstrate that with distant interaction items, the quality of generated alignments are improved on 68 out of 127 query-template pairs in Prosup benchmark. In addition, compared with state-to-art threading methods, our method performs better on alignment accuracy comparison.

Motivation: The challenge of template-based modeling lies in the recognition of correct templates and generation of accurate sequence-template alignments. Homologous information has proved to be very powerful in detecting remote homologs, as demonstrated by the state-of-the-art profile-based method HHpred. However, HHpred does not fare well when proteins under consideration are low-homology. A protein is low-homology if we cannot obtain sufficient amount of homologous information for it from existing protein sequence databases.

Results: We present a profile-entropy dependent scoring function for low-homology protein threading. This method will model correlation among various protein features and determine their relative importance according to the amount of homologous information available. When proteins under consideration are low-homology, our method will rely more on structure information; otherwise, homologous information. Experimental results indicate that our threading method greatly outperforms the best profile-based method HHpred and all the top CASP8 servers on low-homology proteins. Tested on the CASP8 hard targets, our threading method is also better than all the top CASP8 servers but slightly worse than Zhang-Server. This is significant considering that Zhang-Server and other top CASP8 servers use a combination of multiple structure-prediction techniques including consensus method, multiple-template modeling, template-free modeling and model refinement while our method is a classical single-template-based threading method without any post-threading refinement.

The comparative modeling approach to protein structure prediction inherently relies on a template structure. Before building a model such a template protein has to be found and aligned with the query sequence. Any error made on this stage may dramatically affects the quality of result. There is a need, therefore, to develop accurate and sensitive alignment protocols.

Results

BioShell threading software is a versatile tool for aligning protein structures, protein sequences or sequence profiles and query sequences to a template structures. The software is also capable of sub-optimal alignment generation. It can be executed as an application from the UNIX command line, or as a set of Java classes called from a script or a Java application. The implemented Monte Carlo search engine greatly facilitates the development and benchmarking of new alignment scoring schemes even when the functions exhibit non-deterministic polynomial-time complexity.

Conclusions

Numerical experiments indicate that the new threading application offers template detection abilities and provides much better alignments than other methods. The package along with documentation and examples is available at: http://bioshell.pl/threading3d.

Template-based modeling that employs various meta-threading techniques is currently the most accurate, and consequently the most commonly used, approach for protein structure prediction. Despite the evident progress in this field, accurate structure models cannot be constructed for a significant fraction of gene products, thus the development of new algorithms is required. Here, we describe the development, optimization and large-scale benchmarking of eThread, a highly accurate meta-threading procedure for the identification of structural templates and the construction of corresponding target-to-template alignments. eThread integrates ten state-of-the-art threading/fold recognition algorithms in a local environment and extensively uses various machine learning techniques to carry out fully automated template-based protein structure modeling. Tertiary structure prediction employs two protocols based on widely used modeling algorithms: Modeller and TASSER-Lite. As a part of eThread, we also developed eContact, which is a Bayesian classifier for the prediction of inter-residue contacts and eRank, which effectively ranks generated multiple protein models and provides reliable confidence estimates as structure quality assessment. Excluding closely related templates from the modeling process, eThread generates models, which are correct at the fold level, for >80% of the targets; 40–50% of the constructed models are of a very high quality, which would be considered accurate at the family level. Furthermore, in large-scale benchmarking, we compare the performance of eThread to several alternative methods commonly used in protein structure prediction. Finally, we estimate the upper bound for this type of approach and discuss the directions towards further improvements.

We develop and test a new pipeline in CASP10 to predict protein structures based on an interplay of I-TASSER and QUARK for both free-modeling (FM) and template-based modeling (TBM) targets. The most noteworthy observation is that sorting through the threading template pool using the QUARK-based ab initio models as probes allows the detection of distant-homology templates which might be ignored by the traditional sequence profile-based threading alignment algorithms. Further template assembly refinement by I-TASSER resulted in successful folding of two medium-sized FM targets with >150 residues. For TBM, the multiple threading alignments from LOMETS are, for the first time, incorporated into the ab initio QUARK simulations, which were further refined by I-TASSER assembly refinement. Compared with the traditional threading assembly refinement procedures, the inclusion of the threading-constrained ab initio folding models can consistently improve the quality of the full-length models as assessed by the GDT-HA and hydrogen-bonding scores. Despite the success, significant challenges still exist in domain boundary prediction and consistent folding of medium-size proteins (especially beta-proteins) for nonhomologous targets. Further developments of sensitive fold-recognition and ab initio folding methods are critical for solving these problems.

We develop a new threading algorithm MUSTER by extending the previous sequence profile–profile alignment method, PPA. It combines various sequence and structure information into single-body terms which can be conveniently used in dynamic programming search: (1) sequence profiles; (2) secondary structures; (3) structure fragment profiles; (4) solvent accessibility; (5) dihedral torsion angles; (6) hydrophobic scoring matrix. The balance of the weighting parameters is optimized by a grading search based on the average TM-score of 111 training proteins which shows a better performance than using the conventional optimization methods based on the PROSUP data-base. The algorithm is tested on 500 nonhomologous proteins independent of the training sets. After removing the homologous templates with a sequence identity to the target >30%, in 224 cases, the first template alignment has the correct topology with a TM-score >0.5. Even with a more stringent cutoff by removing the templates with a sequence identity >20% or detectable by PSI-BLAST with an E-value <0.05, MUSTER is able to identify correct folds in 137 cases with the first model of TM-score >0.5. Dependent on the homology cutoffs, the average TM-score of the first threading alignments by MUSTER is 5.1–6.3% higher than that by PPA. This improvement is statistically significant by the Wilcoxon signed rank test with a P-value < 1.0 × 10−13, which demonstrates the effect of additional structural information on the protein fold recognition. The MUSTER server is freely available to the academic community at http://zhang.bioinformatics.ku.edu/MUSTER.

This paper presents RaptorX, a statistical method for template-based protein modeling that improves alignment accuracy by exploiting structural information in a single or multiple templates. RaptorX consists of three major components: single-template threading, alignment quality prediction and multiple-template threading. This paper summarizes the methods employed by RaptorX and presents its CASP9 result analysis, aiming to identify major bottlenecks with RaptorX and template-based modeling and hopefully directions for further study. Our results show that template structural information helps a lot with both single-template and multiple-template protein threading especially when closely-related templates are unavailable and there is still large room for improvement in both alignment and template selection. The RaptorX web server is available at http://raptorx.uchicago.edu.

Accurate tertiary structures are very important for the functional study of non-coding RNA molecules. However, predicting RNA tertiary structures is extremely challenging, because of a large conformation space to be explored and lack of an accurate scoring function differentiating the native structure from decoys. The fragment-based conformation sampling method (e.g. FARNA) bears shortcomings that the limited size of a fragment library makes it infeasible to represent all possible conformations well. A recent dynamic Bayesian network method, BARNACLE, overcomes the issue of fragment assembly. In addition, neither of these methods makes use of sequence information in sampling conformations. Here, we present a new probabilistic graphical model, conditional random fields (CRFs), to model RNA sequence–structure relationship, which enables us to accurately estimate the probability of an RNA conformation from sequence. Coupled with a novel tree-guided sampling scheme, our CRF model is then applied to RNA conformation sampling. Experimental results show that our CRF method can model RNA sequence–structure relationship well and sequence information is important for conformation sampling. Our method, named as TreeFolder, generates a much higher percentage of native-like decoys than FARNA and BARNACLE, although we use the same simple energy function as BARNACLE.

Contact: zywang@ttic.edu; j3xu@ttic.edu

Supplementary Information: Supplementary data are available at Bioinformatics online.

Motivation: Protein domains are subunits that can fold and evolve independently. Identification of domain boundary locations is often the first step in protein folding and function annotations. Most of the current methods deduce domain boundaries by sequence-based analysis, which has low accuracy. There is no efficient method for predicting discontinuous domains that consist of segments from separated sequence regions. As template-based methods are most efficient for protein 3D structure modeling, combining multiple threading alignment information should increase the accuracy and reliability of computational domain predictions.

Result: We developed a new protein domain predictor, ThreaDom, which deduces domain boundary locations based on multiple threading alignments. The core of the method development is the derivation of a domain conservation score that combines information from template domain structures and terminal and internal alignment gaps. Tested on 630 non-redundant sequences, without using homologous templates, ThreaDom generates correct single- and multi-domain classifications in 81% of cases, where 78% have the domain linker assigned within ±20 residues. In a second test on 486 proteins with discontinuous domains, ThreaDom achieves an average precision 84% and recall 65% in domain boundary prediction. Finally, ThreaDom was examined on 56 targets from CASP8 and had a domain overlap rate 73, 87 and 85% with the target for Free Modeling, Hard multiple-domain and discontinuous domain proteins, respectively, which are significantly higher than most domain predictors in the CASP8. Similar results were achieved on the targets from the most recently CASP9 and CASP10 experiments.

Availability:
http://zhanglab.ccmb.med.umich.edu/ThreaDom/.

Contact:
zhng@umich.edu

Supplementary information:
Supplementary data are available at Bioinformatics online.

Even when there is agreement on what measure a protein multiple structure alignment should be optimizing, finding the optimal alignment is computationally prohibitive. One approach used by many previous methods is aligned fragment pair chaining, where short structural fragments from all the proteins are aligned against each other optimally, and the final alignment chains these together in geometrically consistent ways. Ye and Godzik have recently suggested that adding geometric flexibility may help better model protein structures in a variety of contexts. We introduce the program Matt (Multiple Alignment with Translations and Twists), an aligned fragment pair chaining algorithm that, in intermediate steps, allows local flexibility between fragments: small translations and rotations are temporarily allowed to bring sets of aligned fragments closer, even if they are physically impossible under rigid body transformations. After a dynamic programming assembly guided by these “bent” alignments, geometric consistency is restored in the final step before the alignment is output. Matt is tested against other recent multiple protein structure alignment programs on the popular Homstrad and SABmark benchmark datasets. Matt's global performance is competitive with the other programs on Homstrad, but outperforms the other programs on SABmark, a benchmark of multiple structure alignments of proteins with more distant homology. On both datasets, Matt demonstrates an ability to better align the ends of α-helices and β-strands, an important characteristic of any structure alignment program intended to help construct a structural template library for threading approaches to the inverse protein-folding problem. The related question of whether Matt alignments can be used to distinguish distantly homologous structure pairs from pairs of proteins that are not homologous is also considered. For this purpose, a p-value score based on the length of the common core and average root mean squared deviation (RMSD) of Matt alignments is shown to largely separate decoys from homologous protein structures in the SABmark benchmark dataset. We postulate that Matt's strong performance comes from its ability to model proteins in different conformational states and, perhaps even more important, its ability to model backbone distortions in more distantly related proteins.

Author Summary

Proteins fold into complicated highly asymmetrical 3-D shapes. When a protein is found to fold in a shape that is sufficiently similar to other proteins whose functional roles are known, this can significantly aid in predicting function in the new protein. In addition, the areas where structure is highly conserved in a set of such similar proteins may indicate functional or structural importance of the conserved region. Given a set of protein structures, the protein structural alignment problem is to determine the superimposition of the backbones of these protein structures that places as much of the structures as possible into close spatial alignment. We introduce an algorithm that allows local flexibility in the structures when it brings them into closer alignment. The algorithm performs as well as its competitors when the structures to be aligned are highly similar, and outperforms them by a larger and larger margin as similarity decreases. In addition, for the related classification problem that asks if the degree of structural similarity between two proteins implies if they likely evolved from a common ancestor, a scoring function assesses, based on the best alignment generated for each pair of protein structures, whether they should be declared sufficiently structurally similar or not. This score can be used to predict when two proteins have sufficiently similar shapes to likely share functional characteristics.

Most pairwise and multiple sequence alignment programs seek alignments with optimal scores. Central to defining such scores is selecting a set of substitution scores for aligned amino acids or nucleotides. For local pairwise alignment, substitution scores are implicitly of log-odds form. We now extend the log-odds formalism to multiple alignments, using Bayesian methods to construct “BILD” (“Bayesian Integral Log-odds”) substitution scores from prior distributions describing columns of related letters. This approach has been used previously only to define scores for aligning individual sequences to sequence profiles, but it has much broader applicability. We describe how to calculate BILD scores efficiently, and illustrate their uses in Gibbs sampling optimization procedures, gapped alignment, and the construction of hidden Markov model profiles. BILD scores enable automated selection of optimal motif and domain model widths, and can inform the decision of whether to include a sequence in a multiple alignment, and the selection of insertion and deletion locations. Other applications include the classification of related sequences into subfamilies, and the definition of profile-profile alignment scores. Although a fully realized multiple alignment program must rely upon more than substitution scores, many existing multiple alignment programs can be modified to employ BILD scores. We illustrate how simple BILD score based strategies can enhance the recognition of DNA binding domains, including the Api-AP2 domain in Toxoplasma gondii and Plasmodium falciparum.

Author Summary

Multiple sequence alignment is a fundamental tool of biological research, widely used to identify important regions of DNA or protein molecules, to infer their biological functions, to reconstruct ancestries, and in numerous other applications. The effectiveness and accuracy of sequence comparison programs depends crucially upon the quality of the scoring systems they use to measure sequence similarity. To compare pairs of DNA or protein sequences, the best strategy for constructing similarity measures has long been understood, but there has been a lack of consensus about how to measure similarity among multiple (i.e. more than two) sequences. In this paper, we describe a natural generalization to multiple alignment of the accepted measure of pairwise similarity. A large variety of methods that are used to compare and analyze DNA or protein molecules, or to model protein domain families, could be rendered more sensitive and precise by adopting this similarity measure. We illustrate how our measure can enhance the recognition of important DNA binding domains.

We developed and tested RAPTOR++ in CASP8 for protein structure prediction. RAPTOR++ contains four modules: threading, model quality assessment, multiple protein alignment and template-free modeling. RAPTOR++ first threads a target protein to all the templates using three methods and then predicts the quality of the 3D model implied by each alignment using a model quality assessment method. Based upon the predicted quality, RAPTOR++ employs different strategies as follows. If multiple alignments have good quality, RAPTOR++ builds a multiple protein alignment between the target and top templates and then generates a 3D model using MODELLER. If all the alignments have very low quality, RAPTOR++ uses template-free modeling. Otherwise, RAPTOR++ submits a threading-generated 3D model with the best quality. RAPTOR++ was not ready for the first 1/3 targets and was under development during the whole CASP8 season. The template-based and template-free modeling modules in RAPTOR++ are not closely integrated. We are using our template-free modeling technique to refine template-based models.

For successful protein structure prediction by comparative modeling, in addition to identifying a good template protein with known structure, obtaining an accurate sequence alignment between a query protein and a template protein is critical. It has been known that the alignment accuracy can vary significantly depending on our choice of various alignment parameters such as gap opening penalty and gap extension penalty. Because the accuracy of sequence alignment is typically measured by comparing it with its corresponding structure alignment, there is no good way of evaluating alignment accuracy without knowing the structure of a query protein, which is obviously not available at the time of structure prediction. Moreover, there is no universal alignment parameter option that would always yield the optimal alignment.

Results

In this work, we develop a method to predict the quality of the alignment between a query and a template. We train the support vector regression (SVR) models to predict the MaxSub scores as a measure of alignment quality. The alignment between a query protein and a template of length n is transformed into a (n + 1)-dimensional feature vector, then it is used as an input to predict the alignment quality by the trained SVR model. Performance of our work is evaluated by various measures including Pearson correlation coefficient between the observed and predicted MaxSub scores. Result shows high correlation coefficient of 0.945. For a pair of query and template, 48 alignments are generated by changing alignment options. Trained SVR models are then applied to predict the MaxSub scores of those and to select the best alignment option which is chosen specifically to the query-template pair. This adaptive selection procedure results in 7.4% improvement of MaxSub scores, compared to those when the single best parameter option is used for all query-template pairs.

Conclusion

The present work demonstrates that the alignment quality can be predicted with reasonable accuracy. Our method is useful not only for selecting the optimal alignment parameters for a chosen template based on predicted alignment quality, but also for filtering out problematic templates that are not suitable for structure prediction due to poor alignment accuracy. This is implemented as a part in FORECAST, the server for fold-recognition and is freely available on the web at

The long-standing problem of constructing protein structure alignments is of central importance in computational biology. The main goal is to provide an alignment of residue correspondences, in order to identify homologous residues across chains. A critical next step of this is the alignment of protein complexes and their interfaces. Here, we introduce the program CMAPi, a two-dimensional dynamic programming algorithm that, given a pair of protein complexes, optimally aligns the contact maps of their interfaces: it produces polynomial-time near-optimal alignments in the case of multiple complexes. We demonstrate the efficacy of our algorithm on complexes from PPI families listed in the SCOPPI database and from highly divergent cytokine families. In comparison to existing techniques, CMAPi generates more accurate alignments of interacting residues within families of interacting proteins, especially for sequences with low similarity. While previous methods that use an all-atom based representation of the interface have been successful, CMAPi's use of a contact map representation allows it to be more tolerant to conformational changes and thus to align more of the interaction surface. These improved interface alignments should enhance homology modeling and threading methods for predicting PPIs by providing a basis for generating template profiles for sequence–structure alignment.

Contact: bab@mit.edu; jbienkowska@gmail.com

Supplementary information: Supplementary data are available at http://theory.csail.mit.edu/cmapi

Most threading methods predict the structure of a protein using only a single template. Due to the increasing number of solved structures, a protein without solved structure is very likely to have more than one similar template structures. Therefore, a natural question to ask is if we can improve modeling accuracy using multiple templates. This paper describes a new multiple-template threading method to answer this question. At the heart of this multiple-template threading method is a novel probabilistic-consistency algorithm that can accurately align a single protein sequence simultaneously to multiple templates. Experimental results indicate that our multiple-template method can improve pairwise sequence-template alignment accuracy and generate models with better quality than single-template models even if they are built from the best single templates (P-value<10-6) while many popular multiple sequence/structure alignment tools fail to do so. The underlying reason is that our probabilistic-consistency algorithm can generate accurate multiple sequence/template alignments. In another word, without an accurate multiple sequence/template alignment the modeling accuracy cannot be improved by simply using multiple templates to increase alignment coverage. Blindly tested on the CASP9 targets with more than one good template structures, our method outperforms all other CASP9 servers except two (Zhang-Server and QUARK of the same group). Our probabilistic-consistency algorithm can possibly be extended to align multiple protein/RNA sequences and structures.

Sequence-based protein homology detection has been extensively studied and so far the most sensitive method is based upon comparison of protein sequence profiles, which are derived from multiple sequence alignment (MSA) of sequence homologs in a protein family. A sequence profile is usually represented as a position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection. This paper presents a new homology detection method MRFalign, consisting of three key components: 1) a Markov Random Fields (MRF) representation of a protein family; 2) a scoring function measuring similarity of two MRFs; and 3) an efficient ADMM (Alternating Direction Method of Multipliers) algorithm aligning two MRFs. Compared to HMM that can only model very short-range residue correlation, MRFs can model long-range residue interaction pattern and thus, encode information for the global 3D structure of a protein family. Consequently, MRF-MRF comparison for remote homology detection shall be much more sensitive than HMM-HMM or PSSM-PSSM comparison. Experiments confirm that MRFalign outperforms several popular HMM or PSSM-based methods in terms of both alignment accuracy and remote homology detection and that MRFalign works particularly well for mainly beta proteins. For example, tested on the benchmark SCOP40 (8353 proteins) for homology detection, PSSM-PSSM and HMM-HMM succeed on 48% and 52% of proteins, respectively, at superfamily level, and on 15% and 27% of proteins, respectively, at fold level. In contrast, MRFalign succeeds on 57.3% and 42.5% of proteins at superfamily and fold level, respectively. This study implies that long-range residue interaction patterns are very helpful for sequence-based homology detection. The software is available for download at http://raptorx.uchicago.edu/download/. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5.

Author Summary

Sequence-based protein homology detection has been extensively studied, but it remains very challenging for remote homologs with divergent sequences. So far the most sensitive methods employ HMM-HMM comparison, which models a protein family using HMM (Hidden Markov Model) and then detects homologs using HMM-HMM alignment. HMM cannot model long-range residue interaction patterns and thus, carries very little information regarding the global 3D structure of a protein family. As such, HMM comparison is not sensitive enough for distantly-related homologs. In this paper, we present an MRF-MRF comparison method for homology detection. In particular, we model a protein family using Markov Random Fields (MRF) and then detect homologs by MRF-MRF alignment. Compared to HMM, MRFs are able to model long-range residue interaction pattern and thus, contains information for the overall 3D structure of a protein family. Consequently, MRF-MRF comparison is much more sensitive than HMM-HMM comparison. To implement MRF-MRF comparison, we have developed a new scoring function to measure the similarity of two MRFs and also an efficient ADMM algorithm to optimize the scoring function. Experiments confirm that MRF-MRF comparison indeed outperforms HMM-HMM comparison in terms of both alignment accuracy and remote homology detection, especially for mainly beta proteins.

In a variety of threading methods, often poorly ranked (low z-score) templates have good alignments. Here, a new method, TASSER_low-zsc that identifies these low z-score ranked templates to improve protein structure prediction accuracy is described. The approach consists of clustering of threading templates by affinity propagation on the basis of structural similarity (thread_cluster) followed by TASSER modeling, with final models selected using a TASSER_QA variant. To establish generality of the approach, templates provided by two threading methods, SP3 and SPARKS2, are examined. The SP3 and SPARKS2 benchmark datasets consist of 351 and 357 medium/hard proteins (those with moderate to poor quality templates and/or alignments) of length ≤ 250 residues respectively. For SP3 medium and hard targets, using thread_cluster, the TM-scores of the best template improve by ~4% and ~9% over the original set (without low z-score templates) respectively; after TASSER modeling/refinement and ranking, the best model improves by ~7% and ~9% over the best model generated with the original template set. Moreover, TASSER_low-zsc generates 22% (43%) more foldable medium (hard) targets. Similar improvements are observed with low ranked templates from SPARKS2. The template clustering approach could be applied to other modeling methods that utilize multiple templates to improve structure prediction.

Assessing the quality of a protein structure model is essential for protein structure prediction. Here, we developed a Support Vector Machine (SVM) method to predict the quality score (GDT-TS score) of a protein structure model from the features extracted from the sequence alignment used to generate the model. We developed a Support Vector Machine (SVM) model quality assessment method, taking either a query-single-template pairwise alignment or a query-multitemplate alignment as input. For the pairwise alignment scheme, the input features fed into the SVM predictor include the normalized e-value of the given alignment, the percentage of identical residue pairs in the alignment, the percentage of residues of the query aligned with those of the template, and the sum of the BLOSUM scores of all aligned residues divided by the length of the aligned positions. Similarly, for the multiple-alignment scheme, the input features include the percentage of the residues of the target sequence aligned with those in one or more templates, the percentage of aligned residues of the target sequence that are the same as that of any one template, the average BLOSUM score of aligned residues and the average Gonnet160 score of aligned residues. A SVM regression predictor was trained on the training data to predict the GDT-TS scores of the models from the input features. The Root Mean Square Error (RMSE) and the Absolute Mean Error (ABS) between predicted and real GDT-TS scores were calculated to evaluate the performance. A five-fold cross validation was applied to select the best parameter values based on the average RMSE and ABS on the five folds. The RMSE and ABS of the optimized SVM predictor on the testing data were close to 0.1. The good performance of the SVM and sequence alignment based predictor indicates that integrating sequence alignment features with a SVM is effective for protein model quality assessment.

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the understanding of their biological role. Although experimental methods can provide detailed information for a small fraction of these proteins, computational modeling is needed for the majority of protein molecules which are experimentally uncharacterized. The I-TASSER server is an on-line workbench for high-resolution modeling of protein structure and function. Given a protein sequence, a typical output from the I-TASSER server includes secondary structure prediction, predicted solvent accessibility of each residue, homologous template proteins detected by threading and structure alignments, up to five full-length tertiary structural models, and structure-based functional annotations for enzyme classification, Gene Ontology terms and protein-ligand binding sites. All the predictions are tagged with a confidence score which tells how accurate the predictions are without knowing the experimental data. To facilitate the special requests of end users, the server provides channels to accept user-specified inter-residue distance and contact maps to interactively change the I-TASSER modeling; it also allows users to specify any proteins as template, or to exclude any template proteins during the structure assembly simulations. The structural information could be collected by the users based on experimental evidences or biological insights with the purpose of improving the quality of I-TASSER predictions. The server was evaluated as the best programs for protein structure and function predictions in the recent community-wide CASP experiments. There are currently >20,000 registered scientists from over 100 countries who are using the on-line I-TASSER server.

We present a profile–profile multiple alignment strategy that uses database searching to collect homologues for each sequence in a given set, in order to enrich their available evolutionary information for the alignment. For each of the alignment sequences, the putative homologous sequences that score above a pre-defined threshold are incorporated into a position-specific pre-alignment profile. The enriched position-specific profile is used for standard progressive alignment, thereby more accurately describing the characteristic features of the given sequence set. We show that owing to the incorporation of the pre-alignment information into a standard progressive multiple alignment routine, the alignment quality between distant sequences increases significantly and outperforms state-of-the-art methods, such as T-COFFEE and MUSCLE. We also show that although entirely sequence-based, our novel strategy is better at aligning distant sequences when compared with a recent contact-based alignment method. Therefore, our pre-alignment profile strategy should be advantageous for applications that rely on high alignment accuracy such as local structure prediction, comparative modelling and threading.

Protein template identification is essential to protein structure and function predictions. However, conventional whole-chain threading approaches often fail to recognize conserved substructure motifs when the target and templates do not share the same fold. We develop a new approach, SEGMER, for identifying protein substructure similarities by segmental threading. The target sequence is split into segments of 2–4 consecutive or non-consecutive secondary structural elements, which are then threaded through PDB to identify appropriate substructure motifs. SEGMER is tested on 144 non-redundant hard proteins. When combined with whole-chain threading, the TM-score of alignments and accuracy of spatial restraints of SEGMER increase by 16% and 25%, respectively, compared to that by the whole-chain threading methods only. When tested on 12 Free Modeling targets from CASP8, SEGMER increases the TM-score and contact accuracy by 28% and 48%, respectively. This significant improvement should have important impact on protein structure modeling and functional inference.